Automatic extraction method of road markings from vehicle-mounted laser scanning point cloud data
With the development of technologies for unmanned driving and smart city construction,it is necessary to efficiently and accurately extract urban road markings. This paper proposed a new method for extracting road markings based on the intensity features and geometric forms of road markings in spatial scenes of vehicle-mounted laser scanning point clouds. This method combined the cloth simulation filter (CSF) algorithm,intensity feature images,and edge detection. First,it utilized the CSF algorithm to extract ground points from the original vehicle-mounted point cloud data. Secondly,the ground points were converted into intensity feature images. Based on the intensity feature images,image edge detection was performed,and connectivity analysis was conducted to extract marked edges. Finally,the Gaussian mixed model was introduced to classify the candidate points for markings and eliminate ground noise points. To test the effectiveness of the algorithm proposed in this article,two different types of road point cloud data were used for experiments. The results show that the average completeness,accuracy,and comprehensive extraction quality of the road marking extraction results by the algorithm proposed in this article are 92.5％,85.9％,and 89.0％,respectively,which are better than those by the comparative model,verifying the reliability and superiority of the algorithm proposed in this paper.
万方数据
维普
